



'''simple docstring'''

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE__     :						int	=  False

class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self					,   _UpperCAmelCase=32						)							->							Any:
					set_seed(0						)
					snake_case__	     =UNetaDModel(sample_size=_UpperCAmelCase					,   in_channels=3					,   out_channels=3						)
					snake_case__	     =torch.optim.SGD(model.parameters()					,   lr=0.0_001						)
					return model, optimizer






			@slow
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     ='cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
					snake_case__	     =DDPMScheduler(
					    num_train_timesteps=1000					,   beta_start=0.0_001					,   beta_end=0.02					,   beta_schedule='linear'					,   clip_sample=_UpperCAmelCase					,   )
					snake_case__	     =DDIMScheduler(
					    num_train_timesteps=1000					,   beta_start=0.0_001					,   beta_end=0.02					,   beta_schedule='linear'					,   clip_sample=_UpperCAmelCase					,   )

					assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

					# shared batches for DDPM and DDIM
					set_seed(0						)
					snake_case__	     =[torch.randn((4, 3, 32, 32)						).clip(-1					,   1						).to(_UpperCAmelCase						) for _ in range(4						)]
					snake_case__	     =[torch.randn((4, 3, 32, 32)						).to(_UpperCAmelCase						) for _ in range(4						)]
					snake_case__	     =[torch.randint(0					,   1000					,   (4,)						).long().to(_UpperCAmelCase						) for _ in range(4						)]

					# train with a DDPM scheduler
					snake_case__     ,	snake_case__	     =self.get_model_optimizer(resolution=32						)
					model.train().to(_UpperCAmelCase						)
					for i in range(4						):
							optimizer.zero_grad()
							snake_case__	     =ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]						)
							snake_case__	     =model(_UpperCAmelCase					,   timesteps[i]						).sample
							snake_case__	     =torch.nn.functional.mse_loss(_UpperCAmelCase					,   noise[i]						)
							loss.backward()
							optimizer.step()
					del model, optimizer

					# recreate the model and optimizer, and retry with DDIM
					snake_case__     ,	snake_case__	     =self.get_model_optimizer(resolution=32						)
					model.train().to(_UpperCAmelCase						)
					for i in range(4						):
							optimizer.zero_grad()
							snake_case__	     =ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]						)
							snake_case__	     =model(_UpperCAmelCase					,   timesteps[i]						).sample
							snake_case__	     =torch.nn.functional.mse_loss(_UpperCAmelCase					,   noise[i]						)
							loss.backward()
							optimizer.step()
					del model, optimizer

					self.assertTrue(torch.allclose(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-5						)						)
					self.assertTrue(torch.allclose(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-5						)						)









'''simple docstring'''

from timeit import timeit






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				number &= number - 1
				result += 1
		return result






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				if number % 2 == 1:
						result += 1
				number >>= 1
		return result






def 				a  (     )       ->	None:

		def do_benchmark(UpperCamelCase_    :    int			) -> None:
				snake_case__	     ='import __main__ as z'
				print(f"""Benchmark when {number = }:"""			)
				print(f"""{get_set_bits_count_using_modulo_operator(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit('z.get_set_bits_count_using_modulo_operator(25)'      ,					setup=UpperCamelCase_			)
				print(f"""timeit() runs in {timing} seconds"""			)
				print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit(
				    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'      ,					setup=UpperCamelCase_      ,					)
				print(f"""timeit() runs in {timing} seconds"""			)

		for number in (25, 37, 58, 0):
				do_benchmark(UpperCamelCase_			)
				print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()




